Summarization of legal documents: Where are we now and the way forward
Due to huge amount of legal information availability on the internet, as well as other sources,
it is important for the research community to do more extensive research on the area of legal …
it is important for the research community to do more extensive research on the area of legal …
Don't give me the details, just the summary! topic-aware convolutional neural networks for extreme summarization
We introduce extreme summarization, a new single-document summarization task which
does not favor extractive strategies and calls for an abstractive modeling approach. The idea …
does not favor extractive strategies and calls for an abstractive modeling approach. The idea …
Neural text summarization: A critical evaluation
Text summarization aims at compressing long documents into a shorter form that conveys
the most important parts of the original document. Despite increased interest in the …
the most important parts of the original document. Despite increased interest in the …
Ranking sentences for extractive summarization with reinforcement learning
Single document summarization is the task of producing a shorter version of a document
while preserving its principal information content. In this paper we conceptualize extractive …
while preserving its principal information content. In this paper we conceptualize extractive …
A unified model for extractive and abstractive summarization using inconsistency loss
WT Hsu, CK Lin, MY Lee, K Min, J Tang… - arXiv preprint arXiv …, 2018 - arxiv.org
We propose a unified model combining the strength of extractive and abstractive
summarization. On the one hand, a simple extractive model can obtain sentence-level …
summarization. On the one hand, a simple extractive model can obtain sentence-level …
DeepSumm: Exploiting topic models and sequence to sequence networks for extractive text summarization
In this paper, we propose DeepSumm, a novel method based on topic modeling and word
embeddings for the extractive summarization of single documents. Recent summarization …
embeddings for the extractive summarization of single documents. Recent summarization …
[HTML][HTML] Summarization of scholarly articles using BERT and BiGRU: Deep learning-based extractive approach
Extractive text summarization involves selecting and combining key sentences directly from
the original text, rather than generating new content. While various methods, both statistical …
the original text, rather than generating new content. While various methods, both statistical …
A comprehensive survey on extractive and abstractive techniques for text summarization
A Mahajani, V Pandya, I Maria, D Sharma - Ambient Communications and …, 2019 - Springer
Over the years as the technology advanced, the amount of data generated during the
simulations and processing has been constantly increasing. Techniques for creating …
simulations and processing has been constantly increasing. Techniques for creating …
Extractive summarization with swap-net: Sentences and words from alternating pointer networks
We present a new neural sequence-to-sequence model for extractive summarization called
SWAP-NET (Sentences and Words from Alternating Pointer Networks). Extractive …
SWAP-NET (Sentences and Words from Alternating Pointer Networks). Extractive …
RankSum—An unsupervised extractive text summarization based on rank fusion
In this paper, we propose Ranksum, an approach for extractive text summarization of single
documents based on the rank fusion of four multi-dimensional sentence features extracted …
documents based on the rank fusion of four multi-dimensional sentence features extracted …